Discover how data analytics drives AI and ML success by optimizing data quality, uncovering insights, and enabling smart decision-making.
Data analytics is the science of analyzing raw data to make conclusions about that information. It involves inspecting, cleaning, transforming, and modeling data to discover useful information, inform conclusions, and support decision-making. In the fields of Artificial Intelligence (AI) and Machine Learning (ML), data analytics is the foundational step that makes it possible to build, train, and evaluate high-performing models. By uncovering patterns, correlations, and anomalies in datasets, analysts provide the critical insights needed to guide a model's development and ensure its effectiveness.
The process typically follows a lifecycle that transforms raw data into actionable insights. This includes data preprocessing to handle missing values and inconsistencies, exploratory data analysis to understand key characteristics, and applying statistical techniques to test hypotheses. The insights derived from analytics are crucial for tasks like feature engineering, selecting appropriate model architectures, and performing hyperparameter tuning. There are generally four types of data analytics, each offering a different level of insight, from looking backward to looking forward. These include descriptive, diagnostic, predictive, and prescriptive analytics, as explained by resources like the Harvard Business School Online.
Data analytics is instrumental in driving progress across numerous AI applications: